This analysis explores relationships between indicators across countries such as percentage of agricultural land, CO2 emissions per capita, and size of surface area using World Bank data. It is divided into two main parts:
We analyze whether the percentage of agricultural land relates to the CO2 emissions per capita. To get an overview over the interested data and be able to to evaluate future insights correctly, we start by looking at the two indicators separately. Starting with the distribution of the CO2 emissions, we get the following information.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.246 8.782 50.328 672.243 232.015 12717.655
## Streuung zwischen den Ländern: 2787843
The CO2 emissions have high variance within the countries.
Simultaneously, there are enormous differences in absolute amounts
between the countries. Therefore, the greatest challenge may lie in
comparing the different countries’ values and trends although the data
is provided on a per capita basis.
Furthermore, the distribution of
the percentage of agricultural land delivers the following
information.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.564 29.805 43.411 42.040 56.129 80.439
## Streuung zwischen den Ländern: 437.7944
In contrast to the CO2 emissions, the percentages of
agricultural land have rather low variance within the countries.
However, there are recognizable deviations between the countries,
spanning from only five to up to eighty percent. As we operate on a
capped percentage scale though, comparisons should be possible quite
well.
Moving on, we want to bring those two variables back
together. For this purpose, analyzing the distribution of the collected
data while disregarding the country-specific origin gives us this cloud
of data points. Note, that the CO2 emissions are displayed logarithmic
to counter the expansive value disparity in the data.
We can recognize a slightly positive linear trend between the
variables. However, the development over time and the
country-specification of observations are completely ignored. In order
to take those factors back into consideration, we first distinguish
among the countries by facetting our visualization for an in depth
comparison of the indicators for each country over time.
The in ascending percentage of agricultural land sorted facets show
no obvious connection between the two indicators, as the CO2 emissions
are developing quite arbitrarily regardless of the associated percentage
of agricultural land.
To dig even further, we now adjust the data
by normalizing the CO2 emissions within each country, letting us
investigate relative changes on the same scale as the agricultural
land.
As we can see, there seems to be no direct relationship between the
countries’ percentage of agricultural land and their CO2 emissions per
capita.
However, one further aspect that might change that non-relationship is the introduction of another variable to take into account, namely the countries’ surface areas.
As we can see, there are several countries with no changes in
surface area throughout the interested timespan at all. Therefore,
before heading forward, we first want to zoom in on those with changes a
little closer.
## Anzahl der Länder ohne Veränderungen: 10
For the vast majority of the countries, the changes can be
classified as under 1000 square kilometers over the whole timespan.
Similar insights can be derived when looking at the relative
changes.
For each country, even those with changes throughout the timespan,
there are at most minimal changes of two percent in surface area. To
finally confirm those claims we take a look at the scatter
decomposition.
## Streuung zwischen den Ländern: 1.783129e+13
In conclusion, we recognize that the changes in surface area are
negligible over time. Therefore, we drop our focus on the development
over time considering this variable when moving on. More interesting
might be shifting the perspective towards whether the absolute amount of
surface area has any influence on the relationship between agricultural
land and CO2 emissions for our subset of countries.
For this
exploration, we want to distinguish our countries into the following
groups:
We see there is no direct influence obvious through the grouping of
the data. Let’s dig deeper by looking at the time-specific distribution.
The biggest anomalies regarding the CO2 emissions with the
percentage of agricultural land in mind seem to be the moderate and very
large surface area countries. Here on one hand, we can detect comparably
high percentages in agricultural land for the moderate area countries,
but those do not transfer themselves to any obvious differences in the
CO2 emissions compared to the other groups. On the other hand, the very
large countries stand out by having the supposedly expectable highest
CO2 emissions among all groups. Marginal differences appear between the
development over time, as the very large area countries are constant
over the two decade timespan, while the other groups have slightly
increasing trends.
If we finally pivot back to our normalized
comparison we did earlier, we can do the same now with our grouped data
according to the surface area categories.
We cannot identify any obvious connection between the CO2
emissions per capita and the percentage of agricultural land even with
the interested countries categorized by surface area.